An intuitive interface to explore and visualize datasets, and
create interactive dashboards.

A wide array of beautiful visualizations to showcase your data.

Easy, code-free, user flows to drill down and slice and dice the data
underlying exposed dashboards. The dashboards and charts acts as a starting
point for deeper analysis.

A state of the art SQL editor/IDE exposing a rich metadata browser, and
an easy workflow to create visualizations out of any result set.

An extensible, high granularity security model allowing intricate rules
on who can access which product features and datasets.
Integration with major
authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, ...)

A lightweight semantic layer, allowing to control how data sources are
exposed to the user by defining dimensions and metrics

Tenemos una nueva Base de Datos abierta, MapD.Este es el mensaje: "The code is available on Github
under an Apache 2.0 license. It has everything you need to build a fully
functional installation of the MapD Core database, enabling sub-second
querying across many billions of records on a multi-GPU server. All of
our core tech, including our tiered caching system and our LLVM query
compilation engine, is contained in today’s open source release"

15 jun. 2017

Our colleagues fromStratebi (analytics specialists), have developed a suite of tools for Pentaho or embed in your own application, that includes:- Improvements in BI Server Console (search, tags...)- OLAP viewer and Adhoc Reporting improved- New tools for end users self service dashboarding

- New amazing scorecard solution on top of Pentaho stack- Powerful predefined real time dashboards- Integration with Big Data technologies

- They are free and you can get open source code

- They only charge support, training and maintenance in order to give you security using this tools in production environments avoiding bugs, including updgrade to new versions (contact with them)

7 jun. 2017

Data. A data warehouse only stores data that has been modeled/structured, while a data lake is no respecter of data. It stores it all—structured, semi-structured, and unstructured. [See my big data is not new graphic. The data warehouse can only store the orange data, while the data lake can store all the orange and blue data.]

Processing. Before we can load data into a data warehouse, we first need to give it some shape and structure—i.e., we need to model it. That’s called schema-on-write. With a data lake, you just load in the raw data, as-is, and then when you’re ready to use the data, that’s when you give it shape and structure. That’s called schema-on-read. Two very different approaches.

Storage. One of the primary features of big data technologies like Hadoop is that the cost of storing data is relatively low as compared to the data warehouse. There are two key reasons for this: First, Hadoop is open source software, so the licensing and community support is free. And second, Hadoop is designed to be installed on low-cost commodity hardware.

Agility. A data warehouse is a highly-structured repository, by definition. It’s not technically hard to change the structure, but it can be very time-consuming given all the business processes that are tied to it. A data lake, on the other hand, lacks the structure of a data warehouse—which gives developers and data scientists the ability to easily configure and reconfigure their models, queries, and apps on-the-fly.

Security. Data warehouse technologies have been around for decades, while big data technologies (the underpinnings of a data lake) are relatively new. Thus, the ability to secure data in a data warehouse is much more mature than securing data in a data lake. It should be noted, however, that there’s a significant effort being placed on security right now in the big data industry. It’s not a question of if, but when.

Users. For a long time, the rally cry has been BI and analytics for everyone! We’ve built the data warehouse and invited “everyone” to come, but have they come? On average, 20-25% of them have. Is it the same cry for the data lake? Will we build the data lake and invite everyone to come? Not if you’re smart. Trust me, a data lake, at this point in its maturity, is best suited for the data scientists.